Owen Wilkins, Co-director
- M.Biol., Molecular & Cellular Biology, University of Bath (UK), 2013
- Ph.D., Program in Experimental and Molecular Medicine, Dartmouth College, 2019
After completing his undergraduate degree in Molecular & Cellular Biology at the University of Bath (UK), Owen joined the Program in Experimental & Molecular Medicine at Dartmouth College. During his graduate career, his thesis work focused on the understanding the contribution of microRNA-related genetic variation to cancer risk and prognosis, during which he developed expertise in the bioinformatic, computational and statistical analysis of a wide range of genomic data types. Collectively, Owen’s technical experience includes analysis of transcriptomics data, DNA-methylation, genetic variation, and assays probing chromatin state (e.g. ATAC-seq, ChIP-seq). Leveraging his background in molecular and cellular biology, Owen is passionate about contributing to multidisciplinary scientific teams in order to find integrative bioinformatic and computational solutions to complex biological problems. Outside of science, Owen enjoys various pursuits in the mountains.
Shannon Soucy, Co-director
- B.S., Microbiology, Biomolecular Sciences, Central Connecticut State University, 2008
- Ph.D., Molecular and Cell Biology, University of Connecticut, 2016
- Postdoctoral fellow, Biological Sciences, Dartmouth College, 2018
Shannon received her doctoral degree from the University of Connecticut where she studied networks of gene transfer in microbial populations to gain insights as to the functional and evolutionary relationships within the community. She continued to study microbial population dynamics in her postdoctoral work, shifting to focus on the “domestication” of a virus and its host bacterium. She expanded this work to start her own lab at River Valley Community College as part of the NH-INBRE program. She has a strong foundation of knowledge regarding microbial ecology, evolution, and functional networks in microbial communities, with a special interest in highly mobile genes (the mobilome). Shannon has worked extensively with comparative genomics, taxonomic data, metagenomic data, and phylogenetic data. She also has a special interest in education and has been teaching beginners to program for many years. Outside of science, she enjoys hiking, gardening, cooking and spending time with her family.
James O’Malley, Faculty Advisor
James O’Malley, MS, Ph.D., is Professor of Biomedical Data Science in The Department of Biomedical Data Science and at The Dartmouth Institute of Health Policy and Clinical Practice at the Geisel School of Medicine at Dartmouth. In 1999 he received his Ph.D. in Statistics from the University of Canterbury, New Zealand and a MS degree along with the L. J. Cote award for excellence in Applied Statistics from Purdue University, USA. His methodological interests encompass social network analysis, multivariate hierarchical models, causal inference using instrumental variables and Bayesian inference with much of his work is motivated by problems in health services research. He has published over 170 peer-reviewed research papers, was chair of the Health Policy Statistics Section (HPSS) of the American Statistical Association (ASA) in 2008 and co-chaired its International Conference in 2011. In 2011 he received the HPSS Mid-career Excellence award, in 2012 was elected to be a fellow of the ASA, and was the 2019 ISPOR (International Society for Pharmacoeconomics and Outcomes Research) Research Excellence Award Recipient in Methodology.
H. Robert Frost, Faculty Advisor
- Ph.D., Quantitative Biomedical Science, Dartmouth College
- B.S., in Mechanical Engineering, Stanford University
Dr. Frost’s research focuses on the development of bioinformatics and biostatistical methods for analyzing high- dimensional genomic data. An important theme of this research has been the use of prior biomedical knowledge, formally encoded in an ontology, to improve statistical power, replication of results, visualization and interpretation. A specific focus of his doctoral studies, postdoctoral work and ongoing K01-funded research is gene set testing or pathway analysis. Gene set testing is an effective hypothesis aggregation method that evaluates hypotheses about biologically related groups of genes, as defined in a resource such as the Molecular Signatures Database (MSigDB). Relative to an approach that tests separate hypotheses for each gene, gene set testing can significantly improve interpretation, statistical power and replication for the analysis of high-dimensional genomic data.
Dr. Frost’s work in this area has included research on new gene set testing methods, research addressing the challenge of annotation quality, research on unsupervised gene set testing and research addressing statistical power. His current gene set testing research focuses on methods for tissue- specific gene set testing and techniques that support gene set analysis of single cell data. Other research interests include cancer genomics, tissue-specific gene function, single cell genomics, gene-environment and gene-gene interaction detection, p-value weighting and screening-testing methods, penalized regression, principal component analysis and random matrix theory.
Tim Sullivan, Research Scientist
- B.S., Mathematics & Biology, Northeastern University 2010
Upon finishing a Bachelors of Science at Northeastern University in 2010, with a double major in Mathematics and Biology, Tim started his journey as a bioinformatican at Dana-Farber Cancer Institute. There, he began learning about the large range of opportunities and challenges presented by so-called Next-Generation Sequencing technologies and the data generated by them. In 2013, he joined the Broad Institute where he participated in the computational analysis of thousands of RNA sequencing samples for the Genotype-Tissue Expression (GTEx) project. Following the Broad Institute, in 2018, he moved to Berlin, Germany, to take a position as a Bioinformatician in the Max Planck Unit for the Science of Pathogens, where he applied his experience in genomics and transcriptomics to the analysis of bacteria, viruses, and CRISPR-edited cells. He is enthusiastic about the flexibility of NGS-based assays and enjoys the thrill of each new dataset. Tim also likes walking around in the mountains and riding his bicycle anywhere it will go.
Noelle N. Kosarek, Research Scientist
Noelle received her PhD in Quantitative Biomedical Sciences at the Geisel School of Medicine at Dartmouth in 2022. Her dissertation work leveraged single cell RNA-sequencing to explore cell type diversity and differentiation in a 3D skin model of systemic sclerosis (SSc), a rare autoimmune disease causing fibrosis of the skin and internal organs. Noelle extended her research interests to include geospatial, epidemiologic investigations of SSc in a United States Medicare beneficiary population. Noelle serves as a research scientist for the data analytics core at Dartmouth where she analyzes single cell datasets for academic labs. Noelle’s interests include implementing novel computational approaches for single cell analysis to discern cell diversity and differentiation in end organs. Outside of the lab Noelle enjoys biking, hiking, running, and skiing in the Upper Valley.